Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
Abstract As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports tha...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35668-6 |
| _version_ | 1797811465215803392 |
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| author | Robert Moss David J. Price Nick Golding Peter Dawson Jodie McVernon Rob J. Hyndman Freya M. Shearer James M. McCaw |
| author_facet | Robert Moss David J. Price Nick Golding Peter Dawson Jodie McVernon Rob J. Hyndman Freya M. Shearer James M. McCaw |
| author_sort | Robert Moss |
| collection | DOAJ |
| description | Abstract As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response. |
| first_indexed | 2024-03-13T07:23:56Z |
| format | Article |
| id | doaj.art-0946c5aa9e394fca828c55406808fd95 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T07:23:56Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0946c5aa9e394fca828c55406808fd952023-06-04T11:28:42ZengNature PortfolioScientific Reports2045-23222023-05-0113111610.1038/s41598-023-35668-6Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020Robert Moss0David J. Price1Nick Golding2Peter Dawson3Jodie McVernon4Rob J. Hyndman5Freya M. Shearer6James M. McCaw7Melbourne School of Population and Global Health, The University of MelbourneMelbourne School of Population and Global Health, The University of MelbourneTelethon Kids InstituteDefence Science and Technology GroupDepartment of Infectious Diseases, Melbourne Medical School, at The Peter Doherty Institute for Infection and ImmunityDepartment of Econometrics and Business Statistics, Monash UniversityMelbourne School of Population and Global Health, The University of MelbourneMelbourne School of Population and Global Health, The University of MelbourneAbstract As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.https://doi.org/10.1038/s41598-023-35668-6 |
| spellingShingle | Robert Moss David J. Price Nick Golding Peter Dawson Jodie McVernon Rob J. Hyndman Freya M. Shearer James M. McCaw Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 Scientific Reports |
| title | Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 |
| title_full | Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 |
| title_fullStr | Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 |
| title_full_unstemmed | Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 |
| title_short | Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 |
| title_sort | forecasting covid 19 activity in australia to support pandemic response may to october 2020 |
| url | https://doi.org/10.1038/s41598-023-35668-6 |
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